Jacques J., Raimondo M. Semi-markov risk models for finance, insurance and reliability
Подождите немного. Документ загружается.
SEMI-MARKOV RISK MODELS FOR
FINANCE, INSURANCE AND
RELIABILITY
SEMI-MARKOV RISK MODELS FOR
FINANCE, INSURANCE AND
RELIABILITY
By
JACQUES JANSSEN
Solvay Business School, Brussels, Belgium
RAIMONDO MANCA
Università di Roma “La Sapienza,” Italy
Library of Congress Control Number:
2006940397
ISBN-10: 0-387-70729-8 e-ISBN: 0-387-70730-1
ISBN-13: 978-0-387-70729-7
Printed on acid-free paper.
AMS Subject Classifications: 60K15, 60K20, 65C50, 90B25, 91B28, 91B30
© 2007 Springer Science+Business Media, LLC
All rights reserved. This work may not be translated or copied in whole or in part without the written
permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY
10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in
connection with any form of information storage and retrieval, electronic adaptation, computer software,
or by similar or dissimilar methodology now known or hereafter developed is forbidden.
The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are
not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject
to proprietary rights.
Printed in the United States of America.
9 8 7 6 5 4 3 2 1
springer.com
Contents
Preface XV
1 Probability Tools for Stochastic Modelling 1
1 The Sample Space 1
2 Probability Space 2
3 Random Variables 6
4 Integrability, Expectation and Independence 8
5 Main Distribution Probabilities 14
5.1 The Binomial Distribution 15
5.2 The Poisson Distribution 16
5.3 The Normal (or Laplace-Gauss) Distribution 16
5.4 The Log-Normal Distribution 19
5.5 The Negative Exponential Distribution 20
5.6 The Multidimensional Normal Distribution 20
6 Conditioning (From Independence to Dependence) 22
6.1 Conditioning: Introductory Case 22
6.2 Conditioning: General Case 26
6.3 Regular Conditional Probability 30
7 Stochastic Processes 34
8 Martingales 37
9 Brownian Motion 40
2 Renewal Theory and Markov Chains 43
1 Purpose of Renewal Theory 43
2 Main Definitions 44
3 Classification of Renewal Processes 45
4 The Renewal Equation 50
5 The Use of Laplace Transform 55
5.1 The Laplace Transform 55
5.2 The Laplace-Stieltjes (L-S) Transform 55
6 Application of Wald’s Identity 56
7 Asymptotical Behaviour of the N(t)-Process 57
8 Delayed and Stationary Renewal Processes 57
9 Markov Chains 58
9.1 Definitions 58
9.2 Markov Chain State Classification 62
9.3 Occupation Times 66
9.4 Computations of Absorption Probabilities 67
9.5 Asymptotic Behaviour 67
VI Contents
9.6 Examples 71
9.7 A Case Study in Social Insurance (Janssen (1966)) 74
3 Markov Renewal Processes, Semi-Markov Processes And
Markov Random Walks 77
1 Positive (J-X) Processes 77
2 Semi-Markov and Extended Semi-Markov Chains 78
3 Primary Properties 79
4 Examples 83
5 Markov Renewal Processes, Semi-Markov and
Associated Counting Processes 85
6 Markov Renewal Functions 87
7 Classification of the States of an MRP 90
8 The Markov Renewal Equation 91
9 Asymptotic Behaviour of an MRP 92
9.1 Asymptotic Behaviour of Markov Renewal Functions 92
9.2 Asymptotic Behaviour of Solutions of Markov Renewal
Equations 93
10 Asymptotic Behaviour of SMP 94
10.1 Irreducible Case 94
10.2 Non-irreducible Case 96
10.2.1 Uni-Reducible Case 96
10.2.2 General Case 97
11 Delayed and Stationary MRP 98
12 Particular Cases of MRP 102
12.1 Renewal Processes and Markov Chains 102
12.2 MRP of Zero Order (PYKE (1962)) 102
12.2.1 First Type of Zero Order MRP 102
12.2.2 Second Type of Zero Order MRP 103
12.3 Continuous Markov Processes 104
13 A Case Study in Social Insurance (Janssen (1966)) 104
13.1 The Semi-Markov Model 104
13.2 Numerical Example 105
14 (J-X) Processes 106
15 Functionals of (J-X) Processes 107
16 Functionals of Positive (J-X) Processes 111
17 Classical Random Walks and Risk Theory 112
17.1 Purpose 112
17.2 Basic Notions on Random Walks 112
17.3 Classification of Random Walks 115
18 Defective Positive (J-X) Processes 117
19 Semi-Markov Random Walks 121
Contents
VII
20 Distribution of the Supremum for Semi-Markov
Random Walks 123
21 Non-Homogeneous Markov and Semi-Markov Processes 124
21.1 General Definitions 124
21.1.1 Completely Non-Homogeneous Semi-Markov
Processes 124
21.1.2 Special Cases 128
4 Discrete Time and Reward SMP and their Numerical Treatment 131
1 Discrete Time Semi-Markov Processes 131
1.1 Purpose 131
1.2 DTSMP Definition 131
2 Numerical Treatment of SMP 133
3 DTSMP and SMP Numerical Solutions 137
4 Solution of DTHSMP and DTNHSMP in the Transient Case:
a Transportation Example 142
4.1. Principle of the Solution 142
4.2. Semi-Markov Transportation Example 143
4.2.1 Homogeneous Case 143
4.2.2 Non-Homogeneous Case 147
5 Continuous and Discrete Time Reward Processes 149
5.1 Classification and Notation 150
5.1.1 Classification of Reward Processes 150
5.1.2 Financial Parameters 151
5.2 Undiscounted SMRWP 153
5.2.1 Fixed Permanence Rewards 153
5.2.2 Variable Permanence and Transition Rewards 154
5.2.3 Non-Homogeneous Permanence and Transition
Rewards 155
5.3 Discounted SMRWP 156
5.3.1 Fixed Permanence and Interest Rate Cases 156
5.3.2 Variable Interest Rates, Permanence
and Transition Cases 158
5.3.3 Non-Homogeneous Interest Rate, Permanence
and Transition Case 159
6 General Algorithms for DTSMRWP 159
7 Numerical Treatment of SMRWP 161
7.1 Undiscounted Case 161
7.2 Discounted Case 163
8 Relation Between DTSMRWP and SMRWP Numerical
Solutions 165
8.1 Undiscounted Case 166
8.2 Discounted Case 168
VIII Contents
5 Semi-Markov Extensions of the Black-Scholes Model 171
1 Introduction to Option Theory 171
2 The Cox-Ross-Rubinstein (CRR) or Binomial Model 174
2.1 One-Period Model 175
2.1.1 The Arbitrage Model 176
2.1.2 Numerical Example 177
2.2 Multi-Period Model 178
2.2.1 Case of Two Periods 178
2.2.2 Case of n Periods 179
2.2.3 Numerical Example 180
3 The Black-Scholes Formula as Limit of the Binomial
Model 181
3.1 The Log-Normality of the Underlying Asset 181
3.2. The Black-Scholes Formula 183
4 The Black-Scholes Continuous Time Model 184
4.1 The Model 184
4.2 The Itô or Stochastic Calculus 184
4.3 The Solution of the Black-Scholes-Samuelson
Model 186
4.4 Pricing the Call with the Black-Scholes-Samuelson
Model 188
4.4.1 The Hedging Portfolio 188
4.4.2 The Risk Neutral Measure and the Martingale
Property 190
4.4.3 The Call-Put Parity Relation 191
5 Exercise on Option Pricing 192
6 The Greek Parameters 193
6.1 Introduction 193
6.2 Values of the Greek Parameters 195
6.3 Exercises 196
7 The Impact of Dividend Distribution 198
8 Estimation of the Volatility 199
8.1 Historic Method 199
8.2 Implicit Volatility Method 200
9 Black and Scholes on the Market 201
9.1 Empirical Studies 201
9.2 Smile Effect 201
10 The Janssen-Manca Model 201
10.1 The Markov Extension of the One-Period
CRR Model 202
10.1.1 The Model 202
10.1.2 Computational Option Pricing Formula for the
One-Period Model 206
Contents
IX
10.1.3 Example 207
10.2 The Multi-Period Discrete Markov Chain Model 209
10.3 The Multi-Period Discrete Markov Chain Limit
Model 211
10.4 The Extension of the Black-Scholes Pricing
Formula with Markov Environment:
The Janssen-Manca Formula 213
11 The Extension of the Black-Scholes Pricing Formula
with Markov Environment: The Semi-Markovian
Janssen-Manca-Volpe formula 216
11.1 Introduction 216
11.2 The Janssen-Manca-Çinlar Model 216
11.2.1 The JMC (Janssen-Manca-Çinlar) Semi-
Markov Model (1995, 1998) 217
11.2.2 The Explicit Expression of S(t) 218
11.3 Call Option Pricing 219
11.4 Stationary Option Pricing Formula 221
12 Markov and Semi-Markov Option Pricing Models with
Arbitrage Possibility 222
12.1 Introduction to the Janssen-Manca-Di Biase
Models 222
12.2 The Homogeneous Markov JMD (Janssen-Manca-
Di Biase) Model for the Underlying Asset 223
12.3 Particular Cases 224
12.4 Numerical Example for the JMD Markov Model 225
12.5 The Continuous Time Homogeneous Semi-Markov
JMD Model for the Underlying Asset 227
12.6 Numerical Example for the Semi-Markov
JMD Model 228
12.7 Conclusion 229
6 Other Semi-Markov Models in Finance and Insurance 231
1 Exchange of Dated Sums in a Stochastic Homogeneous
Environment 231
1.1 Introduction 231
1.2 Deterministic Axiomatic Approach to Financial Choices 232
1.3 The Homogeneous Stochastic Approach 234
1.4 Continuous Time Models with Finite State Space 235
1.5 Discrete Time Model with Finite State Space 236
1.6 An Example of Asset Evaluation 237
1.7 Two Transient Case Examples 238
1.8 Financial Application of Asymptotic Results 244
2 Discrete Time Markov and Semi-Markov Reward Processes
and Generalised Annuities 245
s
X Contents
2.1 Annuities and Markov Reward Processes 246
2.2 HSMRWP and Stochastic Annuities Generalization 248
3 Semi-Markov Model for Interest Rate Structure 251
3.1 The Deterministic Environment 251
3.2 The Homogeneous Stochastic Interest Rate Approach 252
3.3 Discount Factors 253
3.4 An Applied Example in the Homogeneous Case 255
3.5 A Factor Discount Example in the Non-Homogeneous
Case 257
4 Future Pricing Model 259
4.1 Description of Data 260
4.2 The Input Model 261
4.3 The Results 262
5 A Social Security Application with Real Data 265
5.1 The Transient Case Study 265
5.2 The Asymptotic Case 267
6 Semi-Markov Reward Multiple-Life Insurance Models 269
7 Insurance Model with Stochastic Interest Rates 276
7.1 Introduction 276
7.2 The Actuarial Problem 276
7.3 A Semi-Markov Reward Stochastic Interest Rate Model 277
7 Insurance Risk Models 281
1 Classical Stochastic Models for Risk Theory and Ruin
Probability 281
1.1 The G/G or E.S. Andersen Risk Model 282
1.1.1 The Model 282
1.1.2 The Premium 282
1.1.3 Three Basic Processes 284
1.1.4 The Ruin Problem 285
1.2 The P/G or Cramer-Lundberg Risk Model 287
1.2.1 The Model 287
1.2.2 The Ruin Probability 288
1.2.3 Risk Management Using Ruin Probability 293
1.2.4 Cramer’s Estimator 294
2 Diffusion Models for Risk Theory and Ruin Probability 301
2.1 The Simple Diffusion Risk Model 301
2.2 The ALM-Like Risk Model (Janssen (1991), (1993)) 302
2.3 Comparison of ALM-Like and Cramer-Lundberg Risk
Models 304
2.4 The Second ALM-Like Risk Model 305
3 Semi-Markov Risk Models 309
Contents
XI
3.1 The Semi-Markov Risk Model (or SMRM) 309
3.1.1 The General SMR Model 309
3.1.2 The Counting Claim Process 312
3.1.3 The Accumulated Claim Amount Process 314
3.1.4 The Premium Process 315
3.1.5 The Risk and Risk Reserve Processes 316
3.2 The Stationary Semi-Markov Risk Model 316
3.3 Particular SMRM with Conditional
Independence 316
3.3.1 The SM/G Model 317
3.3.2 The G/SM Model 317
3.3.3 The P/SM Model 317
3.3.4 The M/SM Model 318
3.3.5 The M
' /SM Model 318
3.3.6 The SM(0)/SM(0) Model 318
3.3.7 The SM
' (0)/SM '(0) Model 318
3.3.8 The Mixed Zero Order SM
' (0)/SM(0) and
SM(0)/SM
' (0) Models 319
3.4 The Ruin Problem for the General SMRM 320
3.4.1 Ruin and Non-Ruin Probabilities 320
3.4.2 Change of Premium Rate 321
3.4.3 General Solution of the Asymptotic Ruin
Probability Problem for a General SMRM 322
3.5 The Ruin Problem for Particular SMRM 324
3.5.1 The Zero Order Model SM(0)/SM(0) 324
3.5.2 The Zero Order Model SM
' (0)/SM '(0) 325
3.5.3 The Model M/SM 325
3.5.4 The Zero Order Models as Special Case
of the Model M/SM 328
3.6 The M
' /SM Model 329
3.6.1 General Solution 329
3.6.2 Particular Cases: the M/M and M
' /M Models 332
8 Reliability and Credit Risk Models 335
1 Classical Reliability Theory 335
1.1 Basic Concepts 335
1.2 Classification of Failure Rates 336
1.3 Main Distributions Used in Reliability 338
1.4 Basic Indicators of Reliability 339
1.5 Complex and Coherent Structures 340
2 Stochastic Modelling in Reliability Theory 343
2.1 Maintenance Systems 343
2.2 The Semi-Markov Model for Maintenance Systems 346
2.3 A Classical Example 348